Many machine vision systems use geometric descriptions (GD) as canonical models for inspecting or recognizing objects within images. Often, such GD-based models must accurately reflect the physical dimensions of objects so that the exact position of the objects can be determined in an image, and so that precise tolerance checking and defect detection can be performed. For example, vision-based automatic surface mounter (SMD) machines typically employ GD-based models of leaded devices and chips to accurately inspect and place them such that their leads substantially contact the pads of a printed circuit board.
The process of creating accurate GD-based models of objects, either by manually measuring the dimensions of an object, or by extracting the dimensions from manufacturer specifications, can be very tedious, especially when the objects are complicated. Furthermore, there are always new objects to inspect, which means the process of creating an accurate GD-based model must be performed repeatedly. For example, in addition to the many types of leaded components, BGA devices, and chips that SMD mounter manufacturers must describe to create models, there are all sorts of complicated odd-form components, such as coils and connectors.
Existing methods, such as normalized correlation search, typically create featural models (e.g. the 2D spatial arrangement of features such as edges or intensity values based on the appearance of an object), or surface representations (e.g., a 3D volumetric representation of an object's surface) rather than GDs. Thus, there currently exists a need for a method for easily creating GD models.